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Found 5 papers, 3 papers with code
Federated unsupervised random forest for privacy-preserving patient stratification
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data.
Parea: multi-view ensemble clustering for cancer subtype discovery
We apply and validate our methodology on real-world multi-view cancer patient data.
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs
In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs.
Patch augmentation: Towards efficient decision boundaries for neural networks
In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial attacks.
Augmentor: An Image Augmentation Library for Machine Learning
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting.
